Exploring the association between selective serotonin reuptake inhibitors and rhabdomyolysis risk based on the FDA pharmacovigilance database

Rhabdomyolysis is a syndrome potentially fatal and has been associated with selective serotonin reuptake inhibitors (SSRIs) treatment in a few case reports. Herein, we purpose to establish the correlation between SSRIs use and rhabdomyolysis using the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. We conducted an analysis on reports that were submitted to the FAERS database during the period between January 1, 2004, and December 31, 2022. Four algorithms, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM), were employed to quantify the signals of rhabdomyolysis associated with SSRIs. In total, 16,011,277 non-duplicated reports were obtained and analyzed. Among 33,574 reports related to rhabdomyolysis, SSRIs were classified as primary suspected drug in 889 cases. Disproportionality analysis identified a positive signal between rhabdomyolysis and SSRIs (ROR: 2.86, 95% CI 2.67–3.05; PRR: 2.84, χ2: 1037.16; IC0.25 = 1.39; EBGM0.5 = 2.64). Among six SSRIs, fluvoxamine had the strongest signal (ROR: 11.64, 95% CI 8.00–16.93; PRR: 11.38, χ2: 265.51; IC0.25 = 2.41; EBGM0.5 = 8.31), whereas no significant signal of rhabdomyolysis was detected for paroxetine (ROR: 1.83, 95% CI 1.55–2.15; PRR: 1.82, χ2: 53.82; IC0.25 = 0.73; EBGM0.5 = 1.59). After excluding cases co-administered with statins, the signal of rhabdomyolysis associated with SSRIs remains significant. Our analysis reveals that there are differences in safety signals among six SSRIs in respect to the risk of rhabdomyolysis, with fluvoxamine displaying the highest risk signal, while paroxetine did not show a significant signal. Given the potentially lethal nature of rhabdomyolysis, healthcare professionals should inform patients of the potential risk of rhabdomyolysis associated with SSRIs prior to initiating treatment.


Scientific Reports
| (2023) 13:12257 | https://doi.org/10.1038/s41598-023-39482-y www.nature.com/scientificreports/ domyolysis, including 288 cases of sertraline, 2163 cases of fluoxetine, and 30 cases of 145 cases of paroxetine, 136 cases of escitalopram, 129 cases of citalopram and 28 cases of fluvoxamine. As shown in Table 1, sertraline had the highest proportion of SSRIs-related rhabdomyolysis reports (32.40%), followed by fluoxetine (18.34%) and paroxetine (16.31%). Of all the included cases, the majority (56.92%) were between the ages of 18 and 65, with a median age of 39.00 years. Among all the cases with known gender, the proportion of female cases (46.57%) was slightly higher than that of male cases (43.76%). However, in cases related to fluvoxamine, the proportion of male cases was markedly higher than that of female cases. Health professionals, including physicians, pharmacists and other health professionals, submitted 82% of the reports. Among the reporting countries, the highest number of cases (24.18%) was reported from the USA, followed by France (11.70%), the UK (11.25%), Italy (8.77%), and Japan (8.66%).
Outcomes of patients with SSRIs-associated rhabdomyolysis. We conducted a detailed assessment of cases with SSRIs-associated rhabdomyolysis by examining the occurrence of serious outcomes, including death, hospitalization, life-threatening situations, disabilities, required intervention to prevent permanent impairment, and other serious outcomes. As shown in Fig. 3

Discussion
Post-marketing surveillance is necessary to monitor the safety and effectiveness of pharmaceutical and biologic products after they have been approved by regulatory agencies such as the FDA. This is important because clinical trials, which are conducted prior to approval, involve a relatively small and select group of patients and may not detect all possible adverse effects associated with the product's use in a larger population. Post-marketing surveillance enables the identification and evaluation of adverse effects that were not detected during the preapproval stage and allows for prompt action to be taken to protect public health. The FAERS database serves as a vital resource for post-marketing surveillance, allowing researchers to access it freely for pharmacovigilance and pharmacoepidemiologic signal detection studies.
In 2019, the worldwide consumption of SSRIs surpassed that of all other types of antidepressants combined, making them the most widely used antidepressants 17 . This is due to their favorable risk-benefit ratio, which has led to their recommendation as a first-line treatment for psychiatric conditions such as depression and generalized anxiety disorders. The growing number of patients with depression, as well as the surge in psychological disorders associated with the COVID-19 pandemic 18 , are expected to lead to a continued increase in the usage of SSRIs in the future. In light of this trend, it is important to conduct post-marketing surveillance studies to ensure the safety of these drugs. Previous studies have used the FAERS database to elucidate the relationship between SSRIs and suicidality 19 , mania 20 , and postpartum bleeding 21 . Some case reports have suggested that using SSRIs could lead to rhabdomyolysis, a potentially life-threatening syndrome. However, there has not been any comprehensive investigation involving larger patient populations to determine the risk of rhabdomyolysis following the use of SSRIs.
Our analysis of the FAERS database identified 889 cases of rhabdomyolysis associated with the use of SSRIs. The median age of individuals experiencing rhabdomyolysis due to SSRIs was 39.00 years, which aligns with previous research indicating a higher incidence of rhabdomyolysis among patients aged 18-65 years. Specifically, a retrospective cohort study involving 2371 patients found a mean age of 50.7 years among those with rhabdomyolysis 4 . In a multicenter retrospective study involving 387 patients with severe rhabdomyolysis, the median age was found to be 49 years 22 . There was no significant difference in the occurrence rates of SSRIsassociated rhabdomyolysis between females and males. In terms of reporters, 82% of the reports were submitted by healthcare professionals. This may be due to the fact that rhabdomyolysis is a complex disease that requires a systematic assessment by healthcare professionals to make a diagnosis. Furthermore, the fact that 74.47% of hospitalizations due to SSRIs-associated rhabdomyolysis indicates that this condition requires diagnosis and treatment by healthcare professionals in a hospital setting. The fact that the top five reporting countries were all developed nations-USA, France, UK, Italy, and Japan-can be explained in two ways. Firstly, since the FAERS database is an English-language database developed by the FDA in the USA, it is not surprising that the majority of reports come from the USA and other countries where English is the primary language. Secondly, pharmacovigilance is still a relatively new concept and may not be given high priority in developing countries 23 , which could explain why developing countries have lower reporting rates.
The median interval between the start of SSRIs treatment and the onset of rhabdomyolysis was 23.5 days, with a range of 0-135.75 days. Notably, 29.29% of cases experienced rhabdomyolysis on the same day after initiating SSRIs treatment, while 79.80% of cases occurred within the first year of treatment. However, there have been several cases of rhabdomyolysis manifesting up to a decade after the administration of SSRIs, making it difficult to assess the temporal relationship between the two from a pharmacokinetic perspective 11 . Previous studies have reported cases of sertraline-induced rhabdomyolysis after 3 months of therapy 24 , escitalopram-induced rhabdomyolysis after 2 months of therapy 10 , and pravastatin-induced rhabdomyolysis after more than 3 years of therapy 25 . These cases, along with our study, indicate that the risk of drug-induced rhabdomyolysis may persist even after long term use of certain medications. Therefore, it is crucial to continuously monitor patients and remain vigilant for symptoms such as muscle pain, weakness, and dark urine during treatment with SSRIs to identify potential cases of rhabdomyolysis.
The death rate for cases with SSRIs-associated rhabdomyolysis in our study was 13.84%, which is similar to the 12% death rate for rhabdomyolysis reported in all ADRs received by the FAERS during 2017 26 . Actually, the mortality rates reported for rhabdomyolysis exhibit a wide range of variation, from 3.4 to 59%, depending on factors such as the characteristics of the study population and setting, as well as the severity and number of coexisting conditions 27 . Therefore, the reason for the different death rates of rhabdomyolysis caused by different SSRIs www.nature.com/scientificreports/ in our study may be due to differences in the populations taking the medications. However, this requires more cases in the future to investigate population differences in rhabdomyolysis caused by different SSRIs. Patients who experience SSRIs-associated rhabdomyolysis have a high rate of hospitalization primarily because rhabdomyolysis is a medical emergency that often needs hospital admission to receive extracellular volume expansion to prevent AKI. A prior study found that individuals with rhabdomyolysis had a median hospitalization period of 13 days, and the duration of hospitalization was even longer for those who developed AKI.
The results of disproportionality analysis in our work showed that significant signals were detected between rhabdomyolysis and SSRIs treatment. Further analysis confirmed the previously known associations of rhabdomyolysis with escitalopram, fluoxetine, sertraline, citalopram and fluvoxamine as reported in case reports [10][11][12][13][14] . However, our analysis did not find sufficient evidence to suggest a significant signal of paroxetine-associated rhabdomyolysis. On the other hand, fluvoxamine showed the strongest risk signal with an ROR of 11.64. However, it is worth noting that the number of reported cases of fluvoxamine-associated rhabdomyolysis is limited, and further cases are required to verify this finding. Furthermore, while there have been reports of various drugs potentially causing rhabdomyolysis, these are often isolated cases without a well-established association. In contrast, clinical trials have widely demonstrated a clear link between statins and rhabdomyolysis 28 . In our work, after excluding cases where SSRIs were co-administered with statins, we still observed a significant signal of rhabdomyolysis associated with SSRIs. This finding indicates that SSRIs alone can be linked to an increased risk of rhabdomyolysis, independent of statin use.
The precise mechanism by which SSRIs can cause rhabdomyolysis is not yet fully understood. However, both human and rodent studies have revealed that SSRIs affect functional, structural and metabolic properties in skeletal muscle tissue 29 . The SSRIs function by inhibiting the reuptake of serotonin, which leads to an elevation in the concentration of serotonin in the synaptic cleft. Research has shown that rhabdomyolysis is one of the complications of serotonin toxicity 30 . It has been reported that 5-HT receptors have important roles in druginduced rhabdomyolysis and other serotonin toxicity related symptoms. Treatment of zebrafish larvae with an agonist of the 5-HT 2A receptor resulted in a decrease in muscle birefringence and reduced immunostaining for myoseptal and myofibril proteins in skeletal muscle, which were consistent with rhabdomyolysis 31 . Additionally, rhabdomyolysis induced by the serotonin receptor agonist could be prevented by treatment with either a 5-HT 2A antagonist or a 5-HT 2C antagonist 32 . Furthermore, activation of the 5-HT 2A receptor by serotonin or other agonists results in the release of calcium from the intracellular stores, through the coupling of the receptor to a G-protein 33 . Elevated intracellular calcium levels lead to skeletal muscle cell death by activating proteases, intensifying contractility, inducing mitochondrial dysfunction, and increasing reactive oxygen species production 34 . While these studies offer some understanding of how SSRIs may cause rhabdomyolysis, more research is needed to investigate the mechanisms underlying this association.
In conclusion, a disproportionality analysis based on the FAERS has provided additional evidence to support previous case reports, indicating a significant association between certain SSRIs and rhabdomyolysis. Our analysis demonstrates that there are differences in the safety signals of SSRIs regarding the risk of rhabdomyolysis, with fluvoxamine exhibiting the strongest risk signal while paroxetine does not display a significant signal. Therefore, healthcare providers should consider these differences when choosing appropriate medications and inform patients of the potential risk of rhabdomyolysis, especially in those with pre-existing muscle disease or taking medications that may increase the risk of rhabdomyolysis. However, further research is needed to explore the potential mechanisms and risk factors underlying the SSRIs-associated rhabdomyolysis.
There are certain limitations that must be acknowledged in the present work like other pharmacovigilance studies based on the FAERS database. Firstly, given that the FAERS operates as a database for spontaneously reported adverse events, there is a risk of both under-reporting and misreporting. For example, misspelling the name of drugs could lead to this study missing some cases, while inaccuracies in the recording of START_DT and EVENT_DT could result in biases in the time-to-onset results. Secondly, it is widely recognized that the FAERS dataset contains instances of duplicate reports and substantial amounts of missing data. To mitigate this issue, in our analysis, we excluded some duplicate reports by using the CASEID. However, there are also duplicate reports which the same case was submitted by different reporters, resulting in different CASEIDs assigned. Due to the lack of specific characteristics in these reports, it becomes challenging to identify and deduplicate them. Thirdly, a case report may include multiple drugs, which means that cases of SSRIs-associated rhabdomyolysis may involve drugs other than SSRIs. Therefore, to increase the reliability of this study, we only included reports which SSRIs were identified as the PS, and utilized four algorithms to determine the association between SSRIs and rhabdomyolysis. Furthermore, we further strengthened the reliability of our conclusion by conducting an additional analysis after excluding statins, which are the most likely drugs to cause rhabdomyolysis. Lastly, the unavailability of data on the total number of patients using SSRIs makes it difficult to accurately calculate the exact incidence and mortality of SSRIs-associated rhabdomyolysis. Nevertheless, since SSRIs-related rhabdomyolysis is relatively uncommon, our analysis of large database may enhance the level of confidence regarding the association between the use of SSRIs and rhabdomyolysis. This can provide valuable evidence for further research and clinical practice in this field.

Screening of rhabdomyolysis cases related to SSRIs.
To screen for cases of rhabdomyolysis associated with SSRIs, we conducted a four-step analysis. First, we consolidated all relevant reports and removed any duplicates based on FDA-recommended methods. Specifically, we used the most recent FDA_DT when the CASEID was the same, and we chose the highest PRIMARYID when both CASEID and FDA_DT were identical. Next, we screened ADR reports for each SSRI by matching the generic and brand names of the corresponding drugs in the DRUG file. The FAERS categorizes the role of each drug in its associated ADRs as Primary Suspect (PS), Secondary Suspect (SS), Concomitant (C), or Interacting (I). If a drug is classified as PS, it means that the drug is the most likely cause of the adverse drug reaction among all medications taken by the patient. Therefore, to focus our results specifically on the drug most likely responsible for rhabdomyolysis, we restricted our analysis to reports where SSRIs were considered as the PS. We excluded cases in which multiple types of SSRIs were categorized as the PS. Thirdly, we extracted all cases of rhabdomyolysis from the REAC files, using the preferred term "rhabdomyolysis". Finally, we identified all cases of rhabdomyolysis related to the use of SSRIs by taking the intersection of CASEID between the rhabdomyolysis cases and the cases of SSRIs as the PS causing ADRs.

Statistical analysis.
The statistical analysis for this study was conducted using R software version 4.2.0.
Descriptive analysis was used to summarize the demographic and administrative characteristics of SSRIs-associated rhabdomyolysis. Disproportionality analysis are based on a two-by-two contingency table (Table 4).
To measure the association between the use of SSRIs and rhabdomyolysis, four statistical algorithms commonly used in disproportionality analysis were employed: reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayes geometric mean (EBGM). The equations and criteria for these algorithms are presented in Table 5. Previously, Park et al. 35 evaluated various data mining methods for signal detection and found that no single method outperformed the others across all performance measures. They recommended using multiple methods and making decisions based on their collective results for drug-adverse event surveillance. Accordingly, in our work, a safety signal was considered significant only if all four algorithms met their respective criteria.